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heim

fiken-mcp

by heim

fiken_list_contacts

Retrieve a paginated list of your company's customers and suppliers with filters by name, email, date modified, and more.

Instructions

List contacts (customers and suppliers) for a company

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNoFilter by contact name
pageNoPage number, starting at 0
emailNoFilter by email address
groupNoFilter by group name
customerNoFilter to only customers
inactiveNoInclude inactive contacts
pageSizeNoNumber of results per page (max 100)
supplierNoFilter to only suppliers
companySlugYesCompany slug identifier. Use fiken_list_companies to discover available slugs.
lastModifiedNoExact lastModified filter (YYYY-MM-DD)
memberNumberNoFilter by member number
customerNumberNoFilter by customer number
lastModifiedGeNolastModified greater than or equal (YYYY-MM-DD)
lastModifiedGtNolastModified greater than (YYYY-MM-DD)
lastModifiedLeNolastModified less than or equal (YYYY-MM-DD)
lastModifiedLtNolastModified less than (YYYY-MM-DD)
supplierNumberNoFilter by supplier number
organizationNumberNoFilter by organization number
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description fully bears the burden of behavioral disclosure. It only states 'list contacts' and does not disclose pagination, default sorting, potential performance implications, or that it returns a list of objects. This is minimal transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise (7 words) and front-loaded with the core purpose. However, it does not earn its place fully as it omits key context (e.g., pagination, filtering) that could be conveyed in a few extra words without losing conciseness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (18 parameters, no output schema, no annotations), the description is incomplete. It fails to mention return format, pagination behavior, or the fact that filters are available, relying entirely on the schema to fill these gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The description adds no additional meaning beyond the schema—it doesn't explain how filters interact (e.g., customer vs supplier booleans) or the date filter semantics. It neither harms nor significantly helps.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool lists contacts (customers and suppliers) for a company, using a specific verb-resource pair. It distinguishes from sibling list tools that target different entities (e.g., accounts, invoices). However, it does not mention the optional filtering capabilities, which is a minor gap.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives like fiken_get_contact (single contact) or fiken_list_contact_persons. There is no indication of prerequisites or excluded scenarios, leaving the agent to infer usage from context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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